This is where pathology lives. This sort of thinking is fundamental to every test that we perform, every diagnosis that we render. A few sticking points that I (and most people) struggle with:
The essentials of sensitivity and specificity are made memorable by SPIN (specific to rule in) and SNOUT (sensitive to rule out).
| Test Result | Disease | No Disease | Totals |
|---|---|---|---|
| Positive | A | B | A + B |
| Negative | C | D | C + D |
| Totals | A + C | B + D | A + B + C + D |
\[Accuracy = (A+B)/(A+B+C+D)\] \[Sensitivity=A/(A+C)\] \[Specificity=D/(B+D)\] \[Positive Predictive Value=A/(A+B)\] \[Negative Predictive Value=D/(C+D)\]
Let’s see, for instance, how prevalence affects the behavior of a test. Make the following assumptions: Prevalence=0.01 (1%) Sensitivity of our test=90% Specificity of our test=90% We have 1000 patients to examine.
| Test Result | Disease | No Disease | Totals |
|---|---|---|---|
| Positive | 9 | 99 | 108 |
| Negative | 1 | 891 | 892 |
| Totals | 10 | 990 | 1000 |
\(Positive Predictive Value = A/(A+B) = 9/108 = 8.3\)%
\(Negative Predictive Value = D/(C+D) = 891/892 = 99\)%
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
summary(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
##
You can also embed plots, for example:
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
Make sure to comment out your code, but what role comments in RMD documents?
Hey, should I write the GATA3 paper in RMD? Small changes are not worth committing, since a committed document is the “gold standard,” the “display copy,” if you like. Nonetheless, the habit of committing is important.
Why use the git interface in RStudio rather than the terminal in RStudio? Doesn’t seem to add much, and it keeps me slightly further from git…
How do references work in Rmd? Here’s a reference dropped in with citr(1,2). I finally got citr installed when I realized that my R version was not the latest, and was preventing the loading of some dependent packages. Dumb, I know.
YAML specifications including output, bibliography, and CSL are important for inserting citations.
library(tidyverse)
ggplot(data = mpg) +
geom_point(mapping = aes(x = displ, y = hwy, color = class))
Why not make this into a page that contains useful things, like math? I should learn some LATEX(?), anyway.
Images can also be inserted as below.
Images will build up, so are placed in a folder that I set up in the Rproj folder.
1. D&D basic rules: Dungeon master’s basic rules version 0.5. 2018.
2. D&D basic rules: Player’s basic rules version 0.3. 2018.